图 书 馆
具有高斯过程的隐力模型Latent Force Models with Gaussian Processes |
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| 课程网址: | http://videolectures.net/bark08_lawrence_lfmwgp/ |
| 主讲教师: | Neil D. Lawrence |
| 开课单位: | 谢菲尔德大学 |
| 开课时间: | 2008-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We are used to dealing with the situation where we have a latent variable. Often we assume this latent variable to be independently drawn from a distribution, e.g. probabilistic PCA or factor analysis. This simplification is often extended for temporal data where tractable Markovian independence assumptions are used (e.g. Kalman filters or hidden Markov models). In this talk we will consider the more general case where the latent variable is a forcing function in a differential equation model. We will show how for some simple ordinary differential equations the latent variable can be dealt with analytically for particular Gaussian process priors over the latent force. In this talk we will introduce the general framework, present results in systems biology preview extensions. Joint work with Magnus Rattray, Mauricio Alvarez, Pei Gao, Antti Honkela, David Luengo, Guido Sanguinetti and Michalis Titsias. |
| 关 键 词: | 计算机科学; 机器学习; 高斯过程 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-17：lxf |
| 阅读次数: | 46 |